1. Is there a non-geek guide to interpreting LeelaZero's output somewhere?

2. Absent the above, why does the winrate in the starting position (i.e. Black to move) show as something 46% or 47% and at move 2 show as 53%/54%? I'm guessing this is because it thinks 7.5 komi favours White. That would not surprise me, but (assuming my guess is right) what does surprise me is that so little seems to have been made in discussions of such a wide gap. I'd have expected calls for komi to be changed and/or discussions whether Black's play has to be reckless (with all that implies for reliability of evaluations) to overcome such a big discrepancy.

In the position Black d16 + q16, White q4, I see LZ displaying two moves (c4 and d4) as being pondered. Does this really mean it has pruned the list of candidate moves to two? Even if these two are much better than others, is there no easy way of seeing other moves and what they score?

have made me think I should make a video "How to review with Lizzie".

Correct. As the networks improve, they tend to consider fewer moves.

Is that desirable?

Currently Leela Zero and Elf have blind spots that even amateur dan players can detect. Don't they have a parameter that controls exploration?

Edit: For review, if not for play, my inclination would be to have a relatively high degree of exploration, if for nothing else, to see a direct comparison between a number of plays.

But for review, that's not a problem : even if LZ didn't consider your move, when you play it, it will be evaluated and you can see the difference between the board evaluation before you played your move, and after you played it.

An exemple, before the move, LZ don't consider it at all: [...]

And after, LZ evaluate the new situation: [...]

I don't see how more exploration could be more usefull in this case.

It's hard to say for sure, but if their performance improves over time as their exploration decreases (and their policy becomes more sophisticated), I think the burden of proof is on us to show that the decreasing exploration is harmful. Of course it is very easy to come up with individual cases (lots of them!) where it does the wrong thing, but that doesn't mean it is a bad idea in aggregate.


Yes, they do. Tuning it is a delicate matter. My understanding is that when people have tweaked Leela Zero to perform more exploration, it performs worse overall when playing.

This is despite the fact that increasing exploration makes it easier to understand ladders, for example. Don't forget that exploitation is pretty valuable, too!

Edit: For review, if not for play, my inclination would be to have a relatively high degree of exploration, if for nothing else, to see a direct comparison between a number of plays.

I agree that more exploration would be nice, but it's a tough balance to strike, if it actually decreases the strength of the engine overall. Also, the policy of the network has been learned in a symbiotic context with the tree search, which has a certain exploration/exploitation setting, so it's not obvious to me that changing one without the other (by turning up the exploration parameter when analyzing) won't have some unintended side effects.

Indeed. Which is why I say that I don't think that human tweaking of the exploration parameter is the right approach. I would have more confidence in the degree of exploration of Leela Zero and Elf if that parameter were trained, as well as the rest of the bot.

If you are on Windows

What about those who are on MacOS?

Lizzie / Leela Zero works on Mac too, but you need to compile it yourself (which Tami requested not needing to do), there are instructions in the mac release on the page I linked. Quite why there can't be a pre-built executable I don't know, is each Mac really so different? "Mac: It just works"

You should verify if LZ works without lizzie, if it does, the problem is with lizzie, if it don't, the problem is with LZ

Just your terminal, go to your lz folder and type

leelaz --weights=yourWeightsFile --benchmark

Failed to enqueue: 290 kernels
Failed to find a working configuration.
Check your OpenCL drivers.
Minimum error: 100.000000. Error bound: 0.100000
Failed to enqueue: 290 kernels
Failed to find a working configuration.
Check your OpenCL drivers.
Minimum error: 100.000000. Error bound: 0.000100
libc++abi.dylib: terminating with uncaught exception of type std::runtime_error: Tuner failed to find working configuration.
Abort trap: 6